Project Impact

1. d0 0 +  d1 - H1 + 1 X0 Scalar Weight w1  - Weight vector W2 B1 X1 Using a Multi-Stage Wiener Filter for SAR Image Formation in a Distributed Aperture Radar System PI: Dr. Jim Stiles jstiles@rsl.ku.edu GRA: Peng Seng,Tan ptan@ittc.ku.edu Reduced Rank Wiener Filter Project Concept • Using a Non-Uniformly Distributed Aperture Radar System for forming a SAR image will require that a more robust filter than the Matched Filter, i.e. the MMSE or Wiener Filter to be used. • However, using the Wiener Filter involves a computationally expensive matrix inverse. • Hence the research question – Is it possible to develop a more efficient MMSE/Wiener Filter. • The research goal is to reduce the computation time needed to obtain the weight vectors of the MMSE Filter. • This can be achieved by using a lower/reduced rank implementation of the MMSE Filter. The tradeoff of using a lower rank Filter will be between shorter computation time versus better accuracy of predicted data pixel values. • Using this approach, the Multi-Stage Wiener Filter is found to be able to achieve this goal based on the inherent features in its structure. Matched Filter MMSE Filter Project Impact Multi-Stage Wiener Filter • The Multi-Stage Wiener Filter (MSWF) consists of a sequence of decomposition of the initial measurement data vector based on the principle of orthogonal projections. • The idea behind each orthogonal projection/stage is to extract out the residual correlation between the desired pixel data and the rest of the pixels. • It has been determined that the number of decomposition stages required by the MSWF for achieving full rank MMSE performance is less than the full rank of the measured data correlation matrix. • By controlling the number of the decomposition stages, a faster computational time can be obtained using the MSWF. • The implementation of the MSWF can be in scalar format for predicting one data pixel at a time or in vector format for predicting groups of data pixel simultaneously.